“Evolutionary computing” is just as misleading as “artificial neural network”. It would be better if we called evolutionary computing “parallel hill-climbing optimization with information sharing”

a friend passed an assignment using gpt-2 to generate ~3 pages from only 10 sentences. yep, that's the 2020 version of "copy-pasting from wikipedia"

A good example of the human brain using prior knowledge & mental models to fill in gaps of a low-res, noisy sensory input. This works well most times, but can fail on occasion. Useful to think about for AI development and for spread of misinformation on the internet.

If a system contains feedback loops, it will come to be defined by them. To understand a system, look for the feedback loops

By posting GPT generated text we’re polluting the data for its future versions

A language model is just very, very different from a mind, and any overlap in capabilities is an accidental artifact.

Correlation is correlated with causation.

An edited transcript of my talk at Montreal AI Debate 2 is now posted here ucla.in/3amZFmI

A wonderful find! From 1956 - I looked it up. And here's the next paragraph. Of course, GPT-3 doesn't exactly conform to this description. Still ...

Fresh off the press by Jacob Bamberger LTS2 "A topological characterisation of Weisfeiler-Leman equivalence classes" Introduces a dataset of non-isomorphic graphs that cannot be distinguished by message-passing. arxiv.org/abs/2206.11876

Nick Romeo's new book starts with the story of CORE Econ. When I joined CoreEcon in 2016, we believed in “Better Economics for a Better World” & aimed to reform the Economics curriculum. Today, our textbook is used in ~400 colleges, and constantly integrates latest research🎊

Today we’re launching Operator —our first AI agent that can browse the web and complete tasks for you. Available to all Pro users in the US starting today. We’re partnering with top companies to maximize impact, and we’ve built robust safeguards to keep you safe.

I've been loving GPT-4.1, and it's now my daily driver. We've spent hundreds of hours experimenting to get it performing at its best, and I wanted to share some key things we learned to help you get the most out of these models from day 1. Agentic workflows - This is your new

Ambience's RFT-tuned model boosted ICD-10 coding accuracy 27% over expert clinicians—and it's now spotlighted in OpenAI Developers' Reinforcement Fine-Tuning use case guide. Excited to see what the community builds with this powerful new customization technique: platform.openai.com/docs/guides/rf…

new guide on “exploring model graders for reinforcement fine-tuning” on openai’s platform. h/t Theophile Sautory final result was a high-signal, domain-sensitive grader that guided the model toward better explanations. cookbook.openai.com/examples/reinf…

I will be hosting this week's OpenAI build hour this Thurs! This one will be on Image Gen! I will talk about: - cool use cases you can build with it - how you can use it together with other hosted tools - best practices - demo you can use! It will be technical. Builders welcome!